System for predicting game animal movement and managing game animal images

ABSTRACT

A system for managing scouting images, including a scouting camera configured to record and store images of subjects at a particular location and an image management device which has a processor that creates a predictive statement based on image content of the images stored on the scouting camera or copies thereof and a display conveying the predictive statement to a user, wherein the predictive statement indicates a portion of a future time period that subjects are most likely to be at the particular location as compared to remaining portions of the future time period.

RELATED APPLICATIONS

This application is a divisional of patent application Ser. No.12/826,861, filed Jun. 30, 2010, which is based in part on ProvisionalApplication Ser. No. 61/221,992, filed Jun. 30, 2009, the entirety ofthe contents of both applications are incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to game animal scouting cameras and, moreparticularly, to ways of analyzing and manipulating images or data fromsuch scouting cameras and transforming the same into a predictivestatement of game animal movement.

BACKGROUND OF THE INVENTION

Game animal observation for recreation and/or for scouting inassociation with hunting activities is growing increasingly popular.Game animal observation or scouting activities can includeimplementation of scouting cameras for taking photographs, videofootage, or other video recordings. It is known and becomingincreasingly popular to use digital scouting cameras for game animalobservation, allowing users to, for example, save scouting images ontopersonal computers (PCs) for later viewing.

Scouting cameras for scouting potential hunting areas and determininggame patterns, particularly without disturbing animal activity, aregenerally well known in the art. Typically, the apparatus includes afilm, digital or video camera and a passive infrared sensor (e.g., amotion/heat sensor) that is adapted to sense movement and, in response,activate the camera focused on the area in which the sensor detectsmovement. Oftentimes, these devices include a delay timer with multiplesettings to match specific conditions or locations, thus eliminatingunwanted multiple exposures or other non-desired events. Moreover, suchapparatus preferably includes high/low sensitivity settings to allowadjustment of the camera's effective range in order to photograph orrecord game at a desired distance.

Typical digital scouting cameras save images using known image filetypes. This allows users to view the images on their PCs using theimage-viewing software installed on the PCs. The data storage media oftypical digital scouting cameras is recognized by such PCs as being acomputer drive or other ancillary computer device. Accordingly, toretrieve and view the images, users navigate through the computer deviceor file mapping to select the images they want to view or transfer totheir PCs or use one of a variety of image-viewing software packagesthat may be installed on their PCs.

In other words, typical digital scouting cameras require multiple stepsor utilizing multiple software programs to organize and view images. Ifimage enhancement or modification is desired, then even more softwareprograms may be required. This not only can be cumbersome and timeconsuming but at times can be confusing for users.

Besides trying to organize their collected images, game animal observersfrequently engage in their observing and/or hunting activities duringperiods of increased game animal activity. For example, many gameobservers and/or hunters will refer to Solunar Tables (developed by JohnKnight) for estimates of fish and game activity levels based on solarand lunar characteristics on a particular day. Solunar Tables estimatefish and game activity levels based on data that relates to numerousspecies and numerous locations, and, typically, the tables are madeafter considering all days within a calendar year.

However, known Solunar Tables at times do not adequately account forseasonal, location, and species specific variables. Such variablesinclude species specific movement patterns that are influenced bybreeding activity of a species, seasonal food availability at aparticular location, predatory activity at that particular location, andother game-animal-related variables. Stated another way, game animalbehavior is influenced by numerous factors including, for example,time-of-year, breeding seasons, and other factors, which may not besuitably addressed by information presented in known Solunar Tables.

OBJECTS OF THE INVENTION

It is an object of the present invention to provide a system formanaging game animal scouting images that improves the state-of-the-artby overcoming the aforesaid problems of the prior art. Morespecifically, it is an object of the present invention to provide animage management system which reduces the complexity of the userinterface compared to the complexity of known image managing systems. Itis a further object of the present invention to provide a system whichallows full management of the images, enhancement of the images,single-click image classification or sorting, game animal herdevaluations based on the images, and/or predictions based on image herdmovement trends that correspond to image content.

These and other objects of the invention will be apparent from thefollowing descriptions and from the drawings.

SUMMARY OF THE INVENTION

The present invention is a system for managing scouting images whichincludes a scouting camera configured to record and store images ofsubjects at a particular location and an image management device whichitself includes a processor that creates a predictive statement based onimage content of the images stored on the scouting camera or copiesthereof and a display conveying the predictive statement to a user. Insuch inventive system, the predictive statement indicates a portion of afuture time period that subjects are most likely to be at the particularlocation as compared to remaining portions of the future time period. Insome embodiments of the inventive system, the future time period is asingle day.

In preferred embodiments of the inventive system, (a) the subjects aregame animals and the particular location is a particular huntinglocation, (b) the predictive statement indicates relative likelihoods ofgame animals being at the particular hunting location for multipleportions of the future time period, (c) at least some of the multipleportions of the future time period occur within a time period thatcorresponds to a jurisdictionally-defined legal hunting period for thefuture time period, and (d) the relative likelihoods are expressed interms of at least one of a more-or-less indication and a percentageindication.

In additional preferred embodiments of the system for managing scoutingimages, the predictive statement includes relative likelihoods ofsubjects being at the particular location for morning portions,afternoon portions, and evening portions of the future time period. Insome of these preferred embodiments, the predictive statement includesrelative likelihoods of game animals being at the particular huntinglocation for multiple morning portions and multiple evening portions ofthe future time period.

In other preferred embodiments, the processor creates the predictivestatement based on images recorded within a user-defined time period,and in some of these embodiments, the subjects are game animals and theuser-defined time period corresponds to a jurisdictionally definedhunting season. In other of these embodiments, the processor creates thepredictive statement based on occurrences of the user-defined timeperiod over multiple years.

In some preferred embodiments of the inventive system, the predictivestatement includes at least one of lunar information and solarinformation and/or day-length information.

Preferred embodiments of the inventive system may convey globalpositioning information of the particular location on the display.

The present invention is also a method of managing game animal scoutingimages which comprises (a) recording and storing images of game animalswith a game animal scouting camera, (b) transferring the images from thegame animal scouting camera to an image management device, (c) assigningtime-of-day information for each of the images corresponding to atime-of-day when each of the images was recorded, and (d) generating apredictive statement relating to a likelihood of game animal presence ona future time period based on the time-of-day information of the imagesand the number of game animals recorded in the images.

In some preferred embodiments of the inventive method, at least one of(a) the time-of-day information and (b) the number of game animalsrecorded in the images for a user-defined time period are considered togenerate the predictive statement.

Preferred embodiments of the inventive system for managing game animalscouting images have a game animal scouting camera configured to recordand store images of game animals and an image management device whichinclude (a) a memory device that stores images retrieved from the gameanimal scouting camera, (b) a display device for displaying the imagesthereon, (c) multiple classification inputs corresponding to respectiveones of multiple game animal characteristics, the multipleclassification inputs allowing a user to classify the image as showing agame animal having one or more of the multiple game animalcharacteristics, and (d) a sorted database organizing the imagesaccording to the user-classified game animal characteristics of therespective images.

In preferred embodiments of the inventive system, the multipleclassification inputs are provided in a graphical user interface that isdisplayed on the display device. In some of these preferred embodiments,the image management device automatically retrieves the images from thegame animal scouting camera.

The game animal scouting camera assigns time and date information toeach image in other preferred embodiments of the inventive system. Otherpreferred embodiments include image-enhancing inputs allowing a user tomodify characteristics of the images, and some such embodiments,modifying characteristics of an image does not alter suchcharacteristics of a stored copy of the image.

In many preferred embodiments of the inventive system, the imagesinclude still-images and video clips.

In yet additional embodiments of the inventive system, the game animalcharacteristics include gender or age. In some of these embodiments, theimage management device determines and displays a herd statement on thedisplay device based at least in part on the user-classified game animalcharacteristics of the respective images. In some embodiments, the herdstatement conveys a ratio of male game animals to female game animals.In some embodiments, the herd statement conveys a ratio of mature gameanimals to immature game animals. In some embodiments, the herdstatement conveys a ratio of large-antlered/horned game animals tosmall-antlered/horned or non-antlered/horned game animals.

In additional preferred embodiments of the inventive system, the imagemanagement device assigns solar-positioning information to an imagebased at least partially on information relating to the date at whichthe game animal scouting camera recorded the image and the location ofthe game animal scouting camera. In other embodiments, the imagemanagement device assigns solar-positioning information to an imagebased at least partially on information relating to the date at whichthe game animal scouting camera recorded the image and the location ofthe game animal scouting camera. In yet other embodiments, the imagemanagement device assigns lunar-phase information to an image based atleast partially on information relating to the date at which the gameanimal scouting camera recorded the image and the location of the gameanimal scouting camera.

In yet other embodiments, the image management device determines anddisplays a predictive statement on the display device. Such predictivestatement is based at least in part on (a) the user-classified gameanimal characteristics of the respective images, (b) solar-positioninginformation assigned to the images, and (c) the global positioninginformation assigned to the images and the predictive statement conveysinformation relating to a likelihood of a user seeing game animals atsome future time at the same global position at which the images wererecorded.

In many preferred embodiments of the inventive system, the imagemanagement device is a personal computer. Further, in many preferredembodiments, the game animal scouting camera and the image managementdevice are integrated into a single apparatus. Also, in manyembodiments, the image management device utilizes global positioningdata to determine location.

The present invention is also a method of managing game animal scoutingimages which includes the steps of: (a) recording and storing images ofgame animals with a game animal scouting camera; (b) transferring theimages from the game animal scouting camera to an image managementdevice; (c) displaying the images on a display device cooperating withthe image management device; (d) classifying the images based oncharacteristics of game animals visible in the images; and (e)generating a statement relating to game animal herd composition based onimage classification information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a system of the invention.

FIG. 2 is a computer screen shot of a display being used with the systemof FIG. 1.

FIG. 3 is a computer screen shot of the display being used with thesystem of FIG. 1 showing various component options of applications ofthe invention.

FIG. 4 is a computer screen shot of the display being used with thesystem of FIG. 1 showing an image in a main window and showing a list ofsaved images in another window.

FIG. 5 is a variant of the computer screen shot of FIG. 4 showingthumbnail views of the images in the list of saved images.

FIG. 6 is a computer screen shot of the display being used with thesystem of FIG. 1 showing a user interface allowing user date range setupof a herd data module of the invention.

FIG. 7 is a variant of the computer screen shot of FIG. 6 showing a mainscreen of a herd data module after the image related data has beentabulated.

FIGS. 8-12 are computer screen shots of the display being used with thesystem of FIG. 1 showing compiled data charts or tables created by theherd data module.

FIG. 13 is a computer screen shot of the display being used with thesystem of FIG. 1 showing a census report created by the herd datamodule.

FIG. 14 is a computer screen shot of the display being used with thesystem of FIG. 1 showing a calendar used by a prediction module of theinvention for selecting a future date for which a prediction is sought.

FIG. 15 is a computer screen shot of the display being used with thesystem of FIG. 1 showing a prediction statement generated by theprediction module.

FIG. 16 is a block diagram flowchart illustrating a method of managingscouting images of subjects according to a preferred embodiment.

FIG. 17 is a block diagram flowchart illustrating that portion of themethod of FIG. 16 directed to generating a prediction (e.g., apredictive statement).

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

With initial reference to FIG. 1, an embodiment of a system 5 formanaging game animal scouting images and predicting game animal movementis shown. System 5 can manage and enhance videos and/or still photos,collectively referred to herein as “images”. System 5 allows a user toevaluate image contents and sort the images into one or more predefinedor user-defined categories or classifications. Based at least in part onsuch classification(s), system 5 can calculate, generate, or otherwiseperform various herd evaluations. System 5 can use such historic herdevaluations and/or movement trends identified in the images collectivelyto predict future herd movement at a given location based on the imagecontents.

Compared to known image managing systems, inventive system 5 reduces thecomplexity of the user interface, compared to known prior art systems,while allowing, for example, full management of the images, enhancementof the images, single-click image classification or sorting, game animalherd evaluations based on the images, and predictions based on imageherd movement trends that correspond to image content.

As a further description of system 5 generally, the predictive andevaluative functions of system 5 can be fully customizable by the userto adapt to a user's particular requests or goals at a given timebecause the system 5 utilizes a “living” or dynamically-changingdatabase that contains data (e.g., photos) that is location- orproperty-specific. As a user contributes more data or photos to thedatabase, it develops into a more comprehensive representation of gamepresence or movement at the particular location(s) where the photos weretaken. In this regard, system 5 can identify game movement or presencetrends at the particular location and correspondingly predict with ahigh degree of accuracy when and where a subsequent “high activity” daywill occur.

As a continued general overview of system 5, a user can inputinformation into the system 5 by, for example, loading or transferringphotos from a particular location (or, multiple locations on a singleparcel of land, optionally from multiple parcels of land) into system 5.The photos can by classified or labeled, either automatically by thesystem 5 or manually by way of user input(s), so that system 5 canrecognize the photo content, preferably identifying whether such photoscontain game animals and, if so, what type of game animals have beenfound.

In this regard, system 5 creates a hunting or geographicallocation-specific database that is made from the user's own photos.System 5 can use such location-specific database to output defaultreports that show, for example, overall census information as well asthe number of game animal sightings (number of game animals captured inthe user's photos) and display the number of game animal sightings asfunctions of any of a variety of variables, including but not limitedto, solar or lunar information, calendar date, environmental factors,and/or others. These reports can be customized or other reportsgenerated based on the user's preferences or the particular informationthat the user wants to draw or derive from the database.

System 5 can analyze the reports or the database itself to identifytrends or notable occurrences, for example, to identify if there arecertain occasions or periods in which relatively many game animals areseen in the photos, in other words, high activity levels. System 5(and/or user) can correlate such high activity levels to any factorssuch as various time-related, date-related, solar-related,lunar-related, and/or other factors.

If system 5 (or the user) finds a correlation between high game animalactivity levels and some identifiable factor within the reports ordatabase, then system 5 can create a corresponding prediction file thatcharacterizes such correlation and can be used to forecast futureoccurrences of high game animal activity levels. In some embodiments,the prediction file can be an electronic file saved on system 5 thatincludes a table or report that is accessible by a reverse lookup-typeprocedure. In some embodiments, the prediction file can be an electronicfile saved on system 5 that includes an algorithm or mathematicalfunction that expresses the correlation between the high game animalactivity levels and the identified factor in the report or database.

Stated another way, the database used by system 5 is typicallyconfigured as a highly location-specific database, since it is createdsolely or primarily based on photos from a single location, oroptionally from a limited number of locations. This allows system 5 toanalyze game animal herd characteristics at the particular location andpredict game animal presence, movement, or high activity levels at theparticular location based on previous behavior of game animals at suchparticular location. Such prediction can be outputted by system 5 andtherefore conveyed to the user in a predictive statement. The user canreview and consider the predictive statement and correspondingly use thepredictive statement to make certain hunting decisions, such as when tohunt within a hunting season, when to hunt on a particular day, whichparticular location within a parcel of land to hunt, and/or otherdecisions.

Various parameters of game movement or presence trend analyses and trendidentifications or identification of high activity level can be altered,(re)defined, or otherwise modified, adjusted, or customized. In someembodiments, this customizable feature allows a single predictive fileto include different types of predictions and thus allows the system tooutput predictive statements that can convey any of a variety offorecast-type information. The particular forecast-type information thatis outputted to the user in a prediction statement can depend on theuser's particular wants and goals that the user can input into system 5by inputting, manipulating, or adjusting various system parameters(explained in greater detail elsewhere herein), or otherwise. Adjustingsuch parameters allows system 5 to, for example, consider only certaindata and ignore other data while looking for game movement or presencetrends (such trends can be moving trends, changes in rates of sightingoccurrences, and/or others as desired).

Suitable parameters include, but are not limited to, time- ordate-related parameters such as (I) date ranges, (ii) years across whichthe date ranges are considered, (iii) specific calendar dates, (iv)solar status/dates, (v) lunar status/dates, (vi) actual times-of-day,(vii) time proximity with respect to dawn, (viii) time proximity withrespect to dusk, (ix) time proximity with respect to noon, (x) timeproximity with respect to midnight, (xi) and/or other date- ortime-related parameters. The parameters can also include, but are notlimited to, environmental factors such as (I) high or low temperaturesof a day(s), (ii) variations or change in temperatures of a day(s),(iii) actual temperature when photo was taken, (iv) barometric pressure,(v) weather front movement characteristics, (vi) precipitation type,(vii) precipitation intensity, (viii) and/or other environmentalfactors.

Any combinations of the above-listed or other parameters can be used tohelp sort data and to facilitate identifying game movement or presencetrends at the particular location. Furthermore, minimum, maximum,moving, or re-definable threshold values can be used to help define whatdata within the database will be analyzed for identifying game movementor presence trends. For example, data that corresponds to days in whichno bucks or no deer were seen can be disregarded, depending on theparticular type of prediction sought.

Still referring to FIG. 1 and now describing particular components,system 5 includes a game animal scouting camera 10 and an imagemanagement device 15. Scouting camera 10 is configured for use in theoutdoors and able to endure typical weather conditions for relativelyextended periods of time. Suitable scouting cameras are available fromNon Typical, Inc. of DePere, Wis., and are sold under the Cuddeback®brand. Scouting camera 10 triggers or records when game animal motion isdetected, recording images and/or video of such game animals in a memoryblock 12 or other suitable image or data storage medium. After scoutingcamera 10 is deployed for a desired amount of time, for example, one ormore days, one or more weeks, or other periods of time, the images thatare recorded by camera 10 are duplicated on or transferred to imagemanagement device 15, a procedure which is explained in greater detailelsewhere herein.

Still referring to FIG. 1, image management device 15 includes aprocessing block 20 which includes suitable memory, processor(s), and/orother hardware and software for performing the system logic. Processingblock 20 communicates with a display device 25 that is, for example, amonitor or other device for displaying and conveying information tousers. Processing assembly 20 also communicates with and receivesinstructions from a user input system 30 that is configured to allowusers to control operations of the image management device 15. In somepreferred embodiments, the entire image management device 15 isincorporated into a personal computer.

Referring to FIGS. 1-3, user input system 30 can be incorporated ascomponents of a graphical user interface. Accordingly, forimplementations of system in which the image management device 15 isincorporated into or otherwise implemented upon a personal computer, theuser input system can include at least some mouse-clickable buttonsdisplayed on the monitor or display device 25. It is noted that suchmouse-clickable buttons may also be controlled by various keyboard orother functions of the personal computer being used as part of system15.

Referring now to FIGS. 4-5, user input system 30 includes generalcontrols 50, enhancement controls 60, rapid features 70, andapplications 80. The general controls 50 control such basics features ofsystem 5 as, for example, downloading or transferring images from camera10 to image management device 15, file management of the images, andimage-viewing such as advancing or backing up through multiple images,printing images, and/or other functions. For example, general controls50 can include a Get Images button 52 that allows users to transferimages to image management device 15 with a single click. Preferably,clicking Get Image 52 button not only transfers images but also filesthem by default by date, for example, in a separate folder for each yearthat the image was taken according to its image creation date or otherfile characteristic such as an associated date stamp.

As another example of the features within general controls 50, a Printbutton 53 allows for single-click printing. Previous and Next Imagebuttons 54 a and 54 b are provided for sequentially navigating throughthe images. Fast View button 54 c automates the viewing of the images bydisplaying them in a rapidly progressing slide show. Furthermore, Copyand Move buttons 56 and 57 facilitate corresponding file managementfunctions for coping and/or moving selected images into other files ordirectories viewable in one or multiple windows or window segments inthe graphical user interface shown on display device 25. Such multiplewindows or window segments can themselves, or their displayed contents,be controlled by way of, for example, Populate Explorer and View buttons58 and 59.

Still referring to FIGS. 4-5, enhancement controls 60 allow users toalter the images as desired. Enhancement (image-enhancing) controls 60include Darken and Lighten buttons 62 and 64 for adjusting the relativehue or brightness of the images, Zoom In and Zoom Out buttons 65 and 66for magnifying or shrinking the images, and Crop button 67 for removingunwanted portions of the images.

Rapid features 70 allow a user to categorize or classify the images,optionally to save a copy of an image to another location, with a singleclick or input. In some embodiments, rapid features 70 include Buck,Doe, Fawn, and Unknown classifying buttons 72, 73, 74, and 75,respectively. A user views the game animal(s) in an image and clicks onwhich of these classifying buttons 72, 73, 74, and 75 describes thecategory or proper classification of such animal(s). Such assigned imageclassification can be embedded into the image file. Other buttons suchas a Copy to Buck Folder button 76 allows users to create folders thatcontain only images of the respective category.

Referring now to FIGS. 3 and 6, applications 80 allow a user to createand access various herd reports, analyses, and predictions based on theimage content and corresponding lunar, solar, date, and time, and/orotherwise evaluate a particular game animal herd at a particulargeographical location. Applications 80 include a solar/lunar module 82labeled as Astro Time™ showing sun and moon rise and set times, as wellas moon phase, for a particular geographical location and date.

Referring now to FIGS. 6-13, as another feature of applications 80, aherd data module 84 (also see FIG. 3) labeled as CuddeCharts™ isconfigured to generate customized reports for a user-selected timeperiod (FIGS. 6-7). After entering preliminary or setup information toinstruct system 5 which time period and location to analyze (FIGS. 6-7),herd data module 84 can generate and display herd data that shows gameanimal total numbers and breakdowns of gender, maturity, andtime-of-day. Such data can be displayed in terms of traditional oradvanced moon (lunar) indices (FIGS. 8 and 9), or moon percentages (FIG.10), and, optionally, a sunlight index (FIG. 11) or percentage (FIG.12).

Herd data module 84 can display herd census information in a report thatshows population and corresponding breakdowns of gender, maturity,and/or other categories (FIG. 13). Such herd statements provided bysystem 5 can also determine and display information such as a ratio ofmature game animals to immature game animals and/or a ratio of male gameanimals to female game animals. For example, one such piece ofinformation can be a ratio of large-antlered/horned game animals tosmall-antlered/horned or non-antlered/horned game animals. FIG. 13 showssuch exemplary information as a “Buck to Antlerless Ratio.” The resultsof other such possible ratio analyses are also shown in FIG. 13 as a“Buck to Doe Ratio” and a “Fawn to Doe Ratio.”

Referring now to FIGS. 14 and 15, in preferred embodiments, applications80 further include a prediction module 86 (also see FIG. 3) labeled asCrystal Ball™. Prediction module 86 allows a user to upload a predictionfile and generate a herd-specific movement prediction for a particularproperty or geographical location. The prediction statement is generatedbased on past herd movement of the same herd as exemplified by theimages within system 5. Stated another way, prediction module 86 allowsa user to select a future date, and system 5 analyzes the images thereinto identify any previous herd movement tendencies which may indicatewhat the herd activity will likely be at such future date.

In light of the above, and referring again to FIG. 1, to use system 5, aCF (Compact Flash) or SD (Secure Digital) memory card 12 is installed ina suitable game camera, in this embodiment a Cuddeback® scouting camera10. Scouting camera 10 is mounted at a location where game animalrecordation is desired, for example, at or near a hunting location. Oncemounted, scouting camera 10 automatically records images in a knownmanner. When the images are taken, scouting camera 10 embeds date andtime information into each of the images. When the images are to beevaluated, memory card 12 is removed from scouting camera 10 andinserted into a suitable card reader that cooperates with imagemanagement device 15.

Referring now to FIGS. 1-5, the user clicks Get Images button 52 or usesa corresponding command executed by a keyboard, such as a “hot key”command. After the images are transferred from memory card 12 to imagemanagement device 15, memory card 12 can be cleared or erased, ifdesired. At that point, display device 25 will show a list 90 offolders, for example, on the far left portion of a personal computerscreen. The files just copied will be in one or more of thenewly-created folders shown. Double-clicking a folder will open it, anda list 92 of image files will be displayed in the center window.

Double-clicking a single image file will display that image in the largeviewing portion of the personal computer screen. Right-clicking on theimage itself displays viewing or deleting options for that image. Whenan image is being displayed, clicking Previous and Next Image buttons 54a and 54 b will display previous and subsequent images, respectively. Toview the images in a rapid slide show, the user clicks Fast View button54 c. Clicking Fast View button 54 c a second time ends the rapid slideshow.

For managing the folders and images, by default, system 5 stores theimages on the personal computer in a folder labeled, shown in FIG. 2 inthis example as “Cuddeback,” and further within a common-year subfolder“2008.” Thus, in this case, all images loaded in 2008 will be in alocation “C:\Cuddeback\2008.” System 5 will then create new subfoldersin the year subfolder, 2008 in the above example, for each folder copiedfrom memory card 12 using Get Images 52 command. The user can thenrename such new subfolder in any suitable manner, for example, as shownin FIG. 2. Such renaming can be done by right-clicking on the folder tobe renamed.

While viewing individual images, a user can click Print button 53 whichlaunches a print operation through an operating system of the personalcomputer. For example, clicking Print button 53 can launch Microsoft's®Fax Viewer that is included with Windows® XP, but other operatingsystems use other image viewing software. Furthermore, the user canmanipulate enhancement controls 60 to alter how an image is shown ondisplay device 25. For example, if desired, the user can zoom in on animage by left-clicking the mouse while moving the mouse to draw a box.The user can then restore the full image by left-clicking on the image.Optionally, Zoom In and Zoom Out buttons 65 and 66 can be used toaccomplish the same by clicking on the portion of the image that shouldbe zoomed into and then clicking Zoom In or Zoom Out buttons 65 or 66corresponding to the desired function.

Of course, other image altering or enhancements can be accomplished suchas controlling image brightness with Darken and Lighten buttons 62 and64 or cropping an image using Crop button 67. Regardless of whether theimages are altered or enhanced, while viewing them, the user can userapid features 70 to quickly create copies of the images in appropriateones of various categorized folders. Categorization-related informationcan be used by image management device 15 to generate census-typestatements such as buck/doe/fawn ratios within a whitetail dear herd.

Referring now to FIGS. 6-15, after viewing and categorizing the images,a user can then evaluate the herd census or other characteristics andalso make animal behavior predictions using applications 80, such asherd data module 84 (CuddeCharts™ data mapping algorithm) and predictionmodule 86 (Crystal Ball™ prediction software). The user controls,defines, redefines, or otherwise manipulates such evaluation or otherparameters that applications 80 use by selecting, for example, daterange, day/time periods within the date range, and which location orproperty group of photos that the applications 80 will consider whileanalyzing or evaluating.

The below non-limiting examples are generally discussed in terms of moonphase (or other characteristics) or solar position (or othercharacteristics). It is, of course, contemplated and well within thescope of the invention that the same or largely-analogous generalconcepts apply equally for operating the system while considering otherparameters, e.g., non-solar or non-lunar characteristics such as thevarious other parameters mentioned elsewhere herein.

Continuing the discussion of one type of exemplary evaluation oranalysis, image management device 15 reads the image embedded date andtime information and may determine the sun position, moon position, andmoon phase at the time and location where the image was taken. Forexample, image management device 15 can utilize user-entered (globalpositioning system) GPS coordinates and a software-based algorithm todetermine the sun and moon rise/set times at those GPS coordinates.Image management device 15 then determines the moon phase for the daythe image was taken. A moon phase number from 0 to 28, representative ofthe phase of the moon from new moon to first quarter, to full moon, tolast quarter, and back to new moon, is assigned for each image.

For each moon phase day, the number of images taken is tabulated, forexample, using techniques based at least partially on, for example, thepreviously-mentioned Solunar Table methodology for showing periods ofheightened fish and animal movements. Furthermore, data can be tabulatedinto four periods (which are the times the moon is straight east,straight up, straight west, and straight down) for each of the 29 moonphase days. The East and West periods are each two hours long while theUp and Down periods are each one hour long. For each moon phase day, thedata are preferably tabulated into multiple periods of the day thatcorrespond closely to how hunters usually hunt, or typical hunting daysegments.

For example, each moon phase day can be divided into six time periodssuch as: (I) morning (thirty minutes before sunrise to three hours aftersunrise); (ii) late morning (three hours after sunrise to twelveo'clock-noon); (iii) afternoon (twelve o'clock-noon to three hoursbefore sunset); (iv) evening (three hours before sunset to thirtyminutes after sunset); (v) early night (thirty minutes after sunset totwelve o'clock-midnight); and (vi) late night (twelve o'clock-midnightto thirty minutes before sunrise).

Such tables can show a user, for example, a portion of a day (at aparticular moon phase) that has the greatest animal activity level.Preferably, a user can specify a date range to create custom tables, byspecifying a particular range of days that is tabulated and how manyyears the specified date range will be considered. Once the tabulationsare generated, a corresponding predictive report can be saved into acomputer file, indicative of herd movement or game animal presence atvarious times of day, for days within a specified date range, and forparticular moon phases at the particular property or location. Forexample, such predictive reports or files can then be used to predictfuture game animal behavior based on past animal behavior of the sameanimals or at least animals at the same location. The predictive reportor prediction file is created by selecting the File option in herd datamodule 84 (CuddeCharts data mapping algorithm) and selecting “createprediction file” and saving it in a desired location. It is, of course,contemplated that such prediction file creation can instead be automatedand/or automated with user options to customize the prediction filecreation.

Referring now to FIGS. 14 and 15, once at least one prediction file iscreated, predictions can then be generated through prediction module 86(Crystal Ball prediction software). A user clicks a Load Prediction Filebutton 88 a (see FIG. 14) to select and load a desired prediction file,for example, one that corresponds to a date range for which theprediction is sought. The user then selects a future date from acalendar and clicks a Display Prediction button 88 b (FIG. 14).Prediction module 86 determines what the moon phase will be for the dateselected, refers to the moon phase data in the prediction file, and thendisplays such information for the user. This is preferably done in aprediction statement 100 (see FIG. 15) that is shown on display device25 and can be printed in hardcopy by the user. Prediction statement 100can indicate relative likelihoods of game animals being at theparticular hunting location for multiple portions of the selected futureday or other time period.

Referring now to FIGS. 16 and 17, an exemplary prediction method 120 isdescribed. Method 120 shows one technique for prediction generation thatevaluates historical game movement or presence trends at a particularlocation. In some embodiments, this is based on all or selected parts ofthe user's data compilation associated with that location or collectionof locations, for example data relating to photo identifiers andcorresponding game animal activity levels or sighting numbers, alone oras functions of solar information, lunar information, photo identifiers,and/or other factors. In other words, rather than generating aprediction based on theory (such as Solunar Tables), predictions aregenerated based on actual game movement at the location of interest (asrecorded by the user).

It is noted that although the various procedures within method 120 areshown as sequences of blocks, this is merely exemplary and forsimplicity of explanation, whereby procedures within 120 can beperformed in any of a variety of suitable orders or sequences. Exemplarymethod 120 of FIGS. 16 and 17 uses a reverse lookup-type procedure toshow a likelihood of future game movement or presence. Other predictivetechniques besides reverse lookup-type procedures can also be utilizedincluding, but not limited to, creating algorithms or mathematicalfunctions that represent game animal movement or presence trends oroccurrences, for example, how they correlate to one or more of thevarious system parameters or other identifiable factors. In theparticular example of FIGS. 16 and 17, the reverse lookup-type procedureis based on a correlation between a system parameter, moon phase in thisparticular example, of a future date and that or those historically seenat the particular location. Here again, it is, of course, contemplatedand well within the scope of the invention that the same or largelyanalogous general concepts apply equally for creating and outputtingprediction statements while considering other parameters, e.g.,non-solar or non-lunar characteristics such as the various otherparameters mentioned elsewhere herein.

Referring now to FIG. 16, method 120 of system 5 is described in yetanother way, for example, as a sequence of blocks. In Block 122, a userdetermines his ultimate observation or hunting goals which willcorrespond to the type of custom prediction that will be generated. Forexample, a user may know that he has only one weekend (or one timeframe, e.g., mornings) to hunt during a hunting season and wants to seeas many deer as possible during that weekend. He can then use system 5to generate a location-specific prediction of which weekend, forexample, season opening or season closing weekend, he will have arelatively better chance of seeing the greatest number of deer. Or theuser may have only one weekend to hunt during a hunting season and wantsto see as many bucks as possible during that weekend, in which case hecan use system 5 to generate, for example, a location-specificprediction that indicates whether he is more likely to see a lot ofbucks during the season opening or season closing weekend. As anotherexample, if multiple cameras 10 are utilized at different segments orportions of a single location (property), for example, multiple cameras10 near multiple tree stands or other blinds, the user may wish todecide which tree stand to utilize during opening weekend of the hunt.He can then use system 5 to generate a location-specific prediction todetermine in which of the multiple tree stands he will likely see moredeer on a particular day, again based at least in part on historicalinformation and corresponding game animal movement or presence trends onthat particular property.

Referring still to exemplary method 120 of FIG. 16, at or between any ofthe Blocks, the various parameters that are considered can bemanipulated, as represented by Block 123. The images are at some pointtransferred to image management device 15 in an image transfer Block125. As described in greater detail elsewhere herein, the images can bemanipulated as desired at this or at another time during the sequence.Then, during an image classifying Block 130, the user creates an imagedatabase, whereby image content information is saved into an image fileor elsewhere to correspond to such images according to program defaultsettings or the user's preferences. At this point, the images may belabeled as showing, for example, a buck, doe, fawn, or other animal,and/or otherwise identified, for example by location, date, time, andetc. according to program default settings or the user's preferences.Once the images are properly classified, a herd data evaluation oranalysis (Block 135) is performed to generate herd census or other herdinformation or data based on the, for example, number(s) of bucks, does,fawns, or other image subjects on certain days, times, moon phases, andetc. After the herd data is created to generate a prediction file, Block140 can be performed to predict animal movement or presence at a laterdate.

Referring now to FIG. 17, game animal movement or presence predictionBlock 140 of FIG. 16 can itself be described as a sequence of Blocks.First, at Block 145, a prediction file is created which is described ingreater detail elsewhere herein. User input (such as processing onlybuck data) may be accepted in Block 147. A prediction is generated inBlock 150 by, for example, selecting a future date that corresponds toor falls within a time period of historical herd data that was used tocreate the prediction file. A moon phase of the future date isdetermined, and based on such moon phase determination of the futuredate, system 5 recalls (or generates) an animal activity breakdown thatcorresponds to analogous moon phase activity historically determined byand/or stored in the prediction file, in a reverse lookup-type procedureor manner. In some implementations of system 5, prediction generation150 can pull the averaged information from the prediction file and thendisplay that historical analysis as the prediction statement in adisplay Block 160.

For example, again referring to FIGS. 16 and 17, a user can choose todisplay a prediction for the last Saturday in November in the upcomingyear. Assuming that the user has already loaded and classified images onsystem 5, the user can then create a prediction file by selecting thelast week of November (or other nearby desired time period) and a yearrange of the previous five years. System 5 then evaluates and/ortabulates how many deer (or other image category) were seen, duringwhich parts of the day(s), and during which moon phases, throughout thelast week of November during the previous five years. Then the userrequests that a prediction is displayed for the last Saturday ofNovember in the upcoming year. System 5 determines what the moon phasewill be on that particular day and refers to the prediction file to findmatching or analogous (or otherwise corresponding) moon phases duringthe last week of November throughout the previous five years. An averageanimal activity breakdown during the last week of November for theprevious five years is calculated for the matching or analogous (orotherwise corresponding) moon phases, and that average animal activitybreakdown can be displayed as prediction statement 100 (as shown in FIG.15 for a different time period). Thus, prediction statement 100 wouldshow a projected likelihood of seeing deer at a particular location,based on historical data created from images taken at the same location,at the same or similar time of the year, and during a same or similarlunar (or solar) phase.

Prediction statement 100 can be presented in any of a variety ofsuitable ways. For example, prediction statement 100 can be expressed as(I) a more/less likely indication or (ii) a percentage indication, persub-day time period, calculated by dividing the number of deerphotographed during each sub-day time period by the total number of deerphotographed during the entire day(s).

In some embodiments, a prediction check feature is incorporated intosystem 5. In such embodiments, after the actual hunting activity isperformed (for which game animal activity was predicted), the user caninput the accuracy of prediction statement 100 into system 5. In otherwords, users can input whether they indeed saw game animals (orspecifically, bucks, etc.) into the system. If prediction statement 100was accurate, then system 5 can retain the underlying prediction filefor use in generating subsequent prediction statements. However, ifprediction statement 100 was not accurate, then system 5 can delete theprediction file, disregard it for use in generating subsequentprediction statement generations, or preferably modify predictionstatement 100 in some way before generating a subsequent predictionstatement. Prediction files that are shown to be inaccurate during aprediction check feature can be modified according to, for example,program default settings or the user's preferences. In this regard, byutilizing the prediction check feature, a prediction file candynamically change over time based on changes in game animal behavior,activity patterns, and/or other factors that can vary over time at aparticular location.

While the principles of this invention have been described in connectionwith specific embodiments, it should be understood clearly that thesedescriptions are made only by way of example and are not intended tolimit the scope of the invention.

The invention claimed is:
 1. Apparatus for managing scouting images, comprising: a scouting camera configured to record and store images of subjects at a particular location; and an image management device including: a processor configured to create a predictive statement based on image content of the images stored on the scouting camera or copies thereof, the predictive statement indicating a portion of a future time period that subjects are most likely to be at the particular location as compared to remaining portions of the future time period; and a display conveying the predictive statement to a user.
 2. The apparatus of claim 1 wherein the future time period is a single day.
 3. The apparatus of claim 1 wherein: the subjects are game animals and the particular location is a particular hunting location; the predictive statement indicates relative likelihoods of game animals being at the particular hunting location for multiple portions of the future time period; at least some of the multiple portions of the future time period occur within a time period that corresponds to a jurisdictionally-defined legal hunting period for the future time period; and the relative likelihoods are expressed in terms of at least one of a more-or-less indication and a percentage indication.
 4. The apparatus of claim 1 wherein the predictive statement includes relative likelihoods of subjects being at the particular location for morning portions, afternoon portions, and evening portions of the future time period.
 5. The apparatus of claim 4 wherein the predictive statement includes relative likelihoods of game animals being at the particular hunting location for multiple morning portions and multiple evening portions of the future time period.
 6. The apparatus of claim 1 wherein the processor creates the predictive statement based on images recorded within a user-defined time period.
 7. The apparatus of claim 6 wherein the subjects are game animals and the user-defined time period corresponds to a jurisdictionally defined hunting season.
 8. The apparatus of claim 6 wherein the processor creates the predictive statement based on occurrences of the user-defined time period over multiple years.
 9. The apparatus of claim 1 wherein the predictive statement includes at least one of lunar information and solar information.
 10. The apparatus of claim 1 wherein the predictive statement includes day-length information.
 11. The apparatus of claim 1 wherein the display conveys global positioning information of the particular location. 